摘要
针对未知网络攻防场景下,信息不公开导致最优防御策略难以准确选取的问题。通过对不完全信息下的网络攻防博弈进行分析,文章首先构建具有探索机制的攻防演化博弈模型;然后基于Boltzmann探索的Q-learning复制动态方程构建攻防决策动态演化方程;最后通过求解演化稳定均衡给出最优防御策略选取方法,并刻画攻防策略的演化轨迹。仿真实验结果表明,对于小规模局域网,在探索程度参数取10附近时,生成的最优防御策略具有较好的可解释性和稳定性,能够使得防御主体获取最大防御收益。
Aiming at the problem that unknown information makes the optimal defense strategy difficult to select accurately in an unknown network attack and defense scenario.By analyzing the network attack and defense game with incomplete information,firstly,theattack and defense evolutionary game model with an exploration mechanism is constructed.Then,based on Q-learning replication dynamic equationswithexploration of Boltzmann,the dynamic evolution equations ofattack and defense decision are constructed.Finally,the optimal defense strategy selection method is given by solving the evolutionary stable equilibrium,and the evolutionary trajectory of attack and defense strategies are described.The simulation experiment results show that the generated optimal defense strategy has better interpretability and stabilityfor small-scale local area networks,when the exploration degree parameter is around 10,which can enable the defense subject to obtain the maximum defense benefit.
作者
金辉
张红旗
张传富
胡浩
JIN Hui;ZHANG Hongqi;ZHANG Chuanfu;HU Hao(PLA SSF Information Engineering University,Zhengzhou 450004,China;Henan Province Key Laboratory of Information Security,Zhengzhou 450004,China)
出处
《信息网络安全》
CSCD
北大核心
2020年第5期72-82,共11页
Netinfo Security
基金
国家自然科学基金[61902427]。
关键词
网络攻防
不完全信息
演化博弈
Q-learning复制动态方程
最优防御策略
network attack and defense
incomplete information
evolutionary game
Q-learning replication dynamic equation
optimal defense strategy
